Automated Detection of Spammers’ Profiles using Improved K-Means in Twitter

In the digital world of applications, a new application called twitter made a major impact in online social networking and micro blogging. The communication between users is through text based post. Its popularity also attracts many spammers to infiltrate legitimate users account with large amount of spam messages .Online social networking platforms are providing us with a large scale platform to study the human behavior. This paper improves K Means algorithm to separate human from not human users in order to identify normal human activity. K-Means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. Here in this research paper in order to achieve the target results with better accuracy, an efficient approach will be designed by modifying sequential K-Means clustering algorithm to detect spam in Twitter. The data which has been provided for the entire process to be performed is extracted from the social networking website Twitter with the help of R Package as it provides interface with the Twitter web API (Application Programmable Interface). Various calculations has been performed to calculate the accuracy, precision, and recall and based on these results and respective graphs have been obtained.